Computational Framework for Human–AI Symbiosis: A Multidimensional Approach to Augmented Intelligence and Mutual Learning

Human-AI symbiosis focuses on bid irectional augmentation where humans and AI systems co-adapt for improved joint performance. Despite conceptual advances, the field lacks a generalizable computational architecture, rigorous mutual-learning algorithms, and standardized benchmarks for evaluating joint human-AI outcomes. We propose a multidimensional computational framework that integrates an explicit human cognitive model, online mutual-learning algorithms, an interaction manager for adaptive explainability and trust calibration, and privacy-preserving federated update mechanisms. We describe an evaluation plan with benchmark tasks, metrics for joint gains and fairness of benefit, and both short-term and longitudinal user studies. The proposed work aims to provide a reproducible foundation for research and deployment of symbiotic Human-AI systems.